Max Robotics — Global Service Robot Directory + US Certification

About

Lance Zhang is an emerging robotics researcher whose work sits at the intersection of robot learning, simulation, and human-robot interaction. His research tackles one of the field's most pressing challenges: enabling robots to generalize reliably beyond controlled laboratory settings and learn efficiently from limited real-world data. Zhang's most recognized contribution is **RoboCasa**, a large-scale simulation framework designed to train generalist robots on diverse household tasks. By leveraging realistic physical simulation as a scalable alternative to expensive real-world data collection, RoboCasa offers the robotics community a practical pathway for scaling environments, tasks, and training datasets — work that has already garnered 30 combined citations since its 2024 release. Equally impactful is his research on human-in-the-loop autonomy, which proposes keeping human feedback actively integrated during robot deployment to combat brittle generalization and data inefficiency — problems that plague even state-of-the-art deep learning systems. This line of work has accumulated over 35 citations across two publications. Though early in his career, Zhang's focused contributions signal a clear research vision: making robot learning more scalable, adaptive, and practically deployable. Students interested in sim-to-real transfer, interactive robot learning, or generalist robot systems will find his work foundational and forward-looking.